Pattern Recognition of Students' Learning Behavior Based on Deep Learning

Abstract This paper focuses on the application of Deep Learning (DL) technology in education, particularly in the analysis of students' learning behavior patterns. In the context of university education, where students are diverse and come from various disciplines and academic levels, providing education that meets their individual needs is crucial. DL, as an advanced machine learning technology, possesses powerful data analysis and pattern recognition capabilities, which can be leveraged to analyze students' academic performance and learning needs. The research methods involve collecting students' academic data, including test scores, learning history, and learning behavior data, and applying DL algorithms to analyze these data. Through DL technology, we aim to gain a deeper understanding of students' academic abilities and needs, thereby enabling the provision of individualized learning paths and educational support. The application of DL technology has the potential to bring innovation to education and offer students a more individualized and effective learning experience, ultimately better serving the needs of modern education.